| Title | Cosegmentation revisited: Models and optimization |
| Publication Type | Conference Paper |
| Year of Publication | 2010 |
| Authors | Vicente, S, Kolmogorov, V, Rother, C |
| Conference Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| ISBN Number | 3642155510 |
| Abstract | The problem of cosegmentation consists of segmenting the same object (or objects of the same class) in two or more distinct images. Recently a number of different models have been proposed for this problem. However, no comparison of such models and corresponding optimization techniques has been done so far. We analyze three existing models: the L1 norm model of Rother et al. [1], the L2 norm model of Mukherjee et al. [2] and the "reward" model of Hochbaum and Singh [3]. We also study a new model, which is a straightforward extension of the Boykov-Jolly model for single image segmentation [4]. In terms of optimization, we use a Dual Decomposition (DD) technique in addition to optimization methods in [1,2]. Experiments show a significant improvement of DD over published methods. Our main conclusion, however, is that the new model is the best overall because it: (i) has fewest parameters; (ii) is most robust in practice, and (iii) can be optimized well with an efficient EM-style procedure. © 2010 Springer-Verlag. |
| DOI | 10.1007/978-3-642-15552-9_34 |
| Citation Key | Vicente2010 |
